Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
A Survey on Mobile Crowdsensing Systems: Challenges, Solutions, and Opportunities
2019407 citationsAndrea Capponi, Claudio Fiandrino et al.profile →
GreenCloud: a packet-level simulator of energy-aware cloud computing data centers
2010383 citationsDzmitry Kliazovich, Pascal Bouvry et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
This map shows the geographic impact of Pascal Bouvry's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Pascal Bouvry with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Pascal Bouvry more than expected).
This network shows the impact of papers produced by Pascal Bouvry. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Pascal Bouvry. The network helps show where Pascal Bouvry may publish in the future.
Co-authorship network of co-authors of Pascal Bouvry
This figure shows the co-authorship network connecting the top 25 collaborators of Pascal Bouvry.
A scholar is included among the top collaborators of Pascal Bouvry based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Pascal Bouvry. Pascal Bouvry is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
20 of 20 papers shown
1.
Alswaitti, Mohammed, Roberto Verdecchia, Grégoire Danoy, Pascal Bouvry, & Johnatan E. Pecero. (2025). Training Green AI Models Using Elite Samples. IEEE Transactions on Sustainable Computing. 1–16.3 indexed citations
Seredyński, Franciszek, et al.. (2016). Online Bi-Objective Scheduling for IaaS Clouds Ensuring Quality of Service. SIMULATION. 92.2 indexed citations
11.
Guzek, Mateusz, Sébastien Varrette, Valentin Plugaru, Johnatan E. Pecero, & Pascal Bouvry. (2014). A Holistic Model of the Performance and the Energy-Efficiency of Hypervisors in an HPC Environment. Concurrency and Computation Practice and Experience.3 indexed citations
12.
Danoy, Grégoire, et al.. (2013). Optimising Small-World Properties in VANETs with a Parallel Multi-Objective Coevolutionary Algorithm. CLEI electronic journal. 17.
Varrette, Sébastien, et al.. (2011). On the Resilience of [distributed] Evolutionary Algorithms against Cheaters in Global Computing Platforms. Open Repository and Bibliography (University of Luxembourg).1 indexed citations
Danoy, Grégoire, Bernabè Dorronsoro, & Pascal Bouvry. (2011). New State-of-the-art Results for Cassini2 Global Trajectory Optimization Problem. Open Repository and Bibliography (University of Luxembourg).7 indexed citations
18.
Dorronsoro, Bernabè, et al.. (2010). On the Parallelization of Asynchronous Cellular Genetic Algorithms for Multi-core Architectures.. Open Repository and Bibliography (University of Luxembourg).1 indexed citations
19.
Thi, Hoai An Le, Pascal Bouvry, & Tao Pham Dinh. (2008). Modelling, Computation and Optimization in Information Systems and Management Sciences: Second International Conference MCO 2008, Metz, France - Luxembourg, ... in Computer and Information Science). Springer eBooks.2 indexed citations
20.
Bouvry, Pascal, et al.. (1994). Mapping and Load Balancing on Distributed Memory Systems. Open Repository and Bibliography (University of Luxembourg).2 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.